Energy management system with machine learning

ABSTRACT

An energy management system including a universal energy flow manager that has a housing, an energy storage device disposed in the housing, a power electronics module disposed in the housing and adapted to convert and manage power and a connections interface. It also includes distribution and communications module with a HVDC Bus (High-Voltage Direct Current Bus) having variable power limits that powers an entire load requirement of one or more coupled electrical loads up to a defined power limit determined by an aggregation of power from one or more coupled energy sources. The one or more coupled electrical loads and the one or more coupled energy sources are external to the universal energy flow manager and connect to the HVDC Bus through the connections interface.

TECHNICAL FIELD

The present disclosure relates generally to an energy management system.More particularly, the present disclosure relates to modular andscalable energy management system with integrated electronics to supplyand manage a plurality of loads concurrently.

BACKGROUND

Electric automobiles are becoming increasingly popular as more peopleget interested in using renewable and ecologically friendly energyresources such as solar panels. In most circumstances, such technologiesmay be connected to and function with the power grid or residentialelectrical wiring. Furthermore, in regions with high energy costs,consumers may find it more appealing to use an electric vehicle and/orrenewable energy to control costs.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 depicts a block diagram of an energy management environment inwhich illustrative embodiments may be implemented.

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented.

FIG. 3 depicts a block diagram a universal energy flow manager in whichillustrative embodiments may be implemented.

FIG. 4A depicts a sketch of a parallel configuration of universal energyflow managers in which illustrative embodiments may be implemented.

FIG. 4B depicts a sketch of a series configuration of universal energyflow managers in which illustrative embodiments may be implemented.

FIG. 5 depicts a block diagram of an application specific hardwareenvironment in which illustrative embodiments may be implemented.

FIG. 6 depicts a block diagram of an electric vehicle applicationspecific hardware environment in which illustrative embodiments may beimplemented.

FIG. 7 depicts a block diagram of a home application specific hardwareenvironment in which illustrative embodiments may be implemented.

FIG. 8 depicts a block diagram of a universal application specifichardware environment in which illustrative embodiments may beimplemented.

FIG. 9 depicts a sketch of an energy management environment in whichillustrative embodiments may be implemented.

FIG. 10 depicts a flowchart of an energy management process in whichillustrative embodiments may be implemented.

FIG. 11 depicts a sketch of an energy management environment in whichillustrative embodiments may be implemented.

FIG. 12 depicts a block diagram of a machine learning configuration inwhich illustrative embodiments may be implemented.

FIG. 13 depicts a block diagram of a training architecture in whichillustrative embodiments may be implemented.

FIG. 14 depicts a flowchart of a power delivery process in whichillustrative embodiments may be implemented.

FIG. 15 depicts a block diagram of an example prioritization ofattributes in which illustrative embodiments may be implemented.

FIG. 16 depicts a system configured for charge hailing, in accordancewith one or more implementations

FIG. 17 depicts a flowchart of a process in which illustrativeembodiments may be implemented.

FIG. 18 depicts a flowchart of a cloud charge hailing process in whichillustrative embodiments may be implemented.

FIG. 19 depicts a flowchart of an electric vehicle charge hailingprocess in which illustrative embodiments may be implemented.

FIG. 20 depicts a flowchart of a residential charge hailing process inwhich illustrative embodiments may be implemented.

DETAILED DESCRIPTION

The illustrative embodiments recognize that electric vehicles and energysources such as solar panels are becoming increasingly popular as peoplebecome interested in using renewable and ecologically friendly energysources. In some circumstances, such technologies may be connected toand may function with the power grid or residential electrical wiring.Furthermore, in regions with variable electricity pricing for differenttimes of the day, consumers may find it more appealing to use anelectric vehicle and/or solar energy to control energy use andproduction to benefit from lower energy tariffs.

The illustrative embodiments recognize that, solar panels, for example,may have distinct advantages as an energy source that produces DCelectricity while emitting no pollution or emissions. An inverter may beemployed to use this energy with domestic appliances. The illustrativeembodiments recognize that since solar energy production may beunavailable at night, it may be desired to store energy for later use,for example in a battery or other storage system. The illustrativeembodiments recognize that it may be prudent to manage energy storageand consumption with automatically and/or in an intelligent way.

The illustrative embodiments further recognize that electric vehiclesmay draw large amounts of power which may not be readily available inhome energy systems. For example, level 3 power consumption (forexample, greater than 12 kW, or greater than 15 kW) by electric vehiclesconnected to a home energy system may utilize all available grid powerin a home energy system or may cause the entire power budget of anelectrical panel to be surpassed. This may also be true for other highpower electrical appliances such as air conditioners. Even further,supplying a plurality of electric vehicles and high-power electricalappliances with all their power requirements may be significantlydifficult, if not impossible to achieve in a home setting. Theillustrative embodiments recognize that as a result, users may have toprioritize energy use and stagger charging times to cope with theselimitations.

The illustrative embodiments may disclose a universal, modular, andscalable energy management system with integrated electronics toconcurrently supply and manage the entire load requirements of multipleconnected loads in a home or other setting. The illustrative embodimentsmay disclose optimizing energy harvesting and High Voltage DirectCurrent (HVDC) distribution for various applications which may includeresidential and/or automotive applications. The energy management systemmay automatically detect and configure its operating parameters wheninterfacing to multiple loads and sources in efforts of reducingdependence on utility service, avoiding capacity limitations, andreducing downtime cost. The energy management system may furtherintroduce DC (Direct Current) Fast Charging into residential markets andpromote the development of smart homes. Not only may electric vehiclesor high-power electrical appliances be DC Fast Charged in home energysystems, a plurality of electric vehicles and high-power electricalappliances may also be DC Fast Charged concurrently. The illustrativeembodiments may disclose a universal energy flow manager that mayreceive any number of energy sources for routing to any number of loads.The ability to obtain large amounts of power may only be limited byenergy sources that are available to be connected. This may increasecustomer adoption of renewable energy sources by providingless-expensive option as well as by eliminating complexity resultingfrom different manufacturers, component specifications, andinstallation. The energy management system may thus provide smart homesafety measures and automation, energy cycling and resale, backup energyoptimization, prolonged supply durations, convenience andself-installation.

In an aspect, the energy management system may comprise a universalenergy flow manager comprising a housing, an energy storage devicedisposed in the housing, a power electronics module disposed in thehousing and configured to convert and manage power and a connectionsinterface. The universal energy flow manager may also comprise adistribution and communications module comprising at least a HVDC Bus(High-Voltage Direct Current Bus) having variable power limits,configured to power an entire load requirement of one or more coupledelectrical loads up to a defined power limit determined by anaggregation of power from one or more coupled energy sources. Thisaggregation may be achieved, for example, by the parallel connection ofall low voltage application specific hardware (ASH) for a low voltage DCBus or the parallel connection of all high voltage application specifichardware for the high voltage DC Bus, as discussed hereinafter. The oneor more coupled electrical loads and the one or more coupled energysources may be external to the universal energy flow manager and mayconnect to the HVDC Bus through the connections interface. Byconfiguring the energy flow manager to power an entire load requirementof one or more coupled electrical loads up to a defined power limitdetermined by an aggregation of power from any number of coupled energysources and types, the energy flow manager may be “universal” to providepower to any type of connected load, from any type of connected sourceregardless of load or source architecture. Thus, the energy sources maynot be required to have output energy/power specifications that matchthat of the universal energy flow manager.

In another aspect, EV-to-EV DC (electric vehicle to electric vehicleDirect Current) Fast Charging may be provided. Further, multiple EVs maybe concurrently charged using energy time-division multiplexing. Herein,routing of energy/power from an energy source to an EV load over acommon connection may be performed by allocating a full transmissionoperation to one source and/or one EV at a time, for a certain durationof time. This may be performed to enable power transfer between asource-load pair without needed a dedicated connection for each pair.

In another aspect, a portable energy bank may be provided wherein DCFast charging for low voltage DC equipment and emergency/roadsideassistance capability may also be available via the on-board energystorage device of the energy flow manager.

In another aspect, an intelligent proposal of one or more power deliveryproposal operations may be disclosed. The intelligent proposal maycomprise the steps of receiving an energy demand state of one or morecoupled electrical loads coupled to a universal energy flow manager viaa load application specific hardware (load ASH), the energy demand statebeing indicative of a desired amount of energy needed by the one or morecoupled electrical loads in an energy management environment. Theintelligent proposal may further comprise receiving an available energystate of one or more coupled energy sources coupled to the universalenergy flow manager via a source application specific hardware (sourceASH), the available energy state being indicative of an available amountof energy from the one ore one or more coupled energy sources in theenergy management environment. Input data may be generated using atleast the energy demand and available energy states for use by a powerdelivery module and one or more features may be extracted from the inputdata, the one or more features being representative of a characteristicof a request for a power delivery proposal operation. At least one powerdelivery proposal may be proposed by the power delivery module andimplemented for the one or more coupled electrical loads. The powerdelivery module may operate a machine learning engine.

The architecture and manner of managing energy sources and loads isunavailable in the presently available methods in the technologicalfield of endeavor pertaining to battery energy storage systems andelectric vehicles. The term electric vehicle is used herein tocollectively vehicles and appliances such as motor vehicles, railedvehicles, watercraft, home appliances and aircraft that are configuredto utilize rechargeable electric batteries as their main source ofenergy to power their component systems or for propulsion. Theillustrative embodiments are also described with respect to certaintypes of data, functions, algorithms, equations, model configurations,locations of embodiments, additional data, devices, data processingsystems, environments, components, and applications only as examples.Any specific manifestations of these and other similar artifacts are notintended to be limiting to the disclosure. Any suitable manifestation ofthese and other similar artifacts can be selected within the scope ofthe illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the disclosure, either locally at a dataprocessing system or over a data network, within the scope of thedisclosure. Where an embodiment is described using a client device, anytype of data storage device suitable for use with the client device mayprovide the data to such embodiment, either locally at the client deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the disclosure within the scope ofthe disclosure. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIG. 1, this figure is an example diagram of an energy management environment100 in which illustrative embodiments may be implemented. FIG. 1 is onlyan example and is not intended to assert or imply any limitation withregard to the environments in which different embodiments may beimplemented. A particular implementation may make many modifications tothe depicted environments based on the following description.

Energy management environment 100 is a network of energy managementsystems comprising a universal energy flow manager 126 having anapplication 136. The energy management environment 100 may also comprisecomputers in which the illustrative embodiments may be implemented, andenergy sources such as a power grid 128, renewable energy sources 130,and electric vehicles 132. The energy management environment 100 mayalso comprise electrical loads (including, for example, other electricvehicles 132, and home appliances) and network/communicationinfrastructure 102. Network/communication infrastructure 102 may be themedium used to provide communications links between various devices,databases and computers connected together within energy managementenvironment 100. Network/communication infrastructure 102 may includeconnections such as CAN (Controller Area Network) Bus connections, PLC(Programmable logic controller), wires, wireless communication links, orfiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network/communication infrastructure 102 and arenot intended to exclude other configurations or roles for these dataprocessing systems. Server 104 and server 106 couple tonetwork/communication infrastructure 102 along with storage unit 108comprising database 118. Software applications may execute on anycomputer in energy management environment 100. Client 110 and dashboard112 are also coupled to network/communication infrastructure 102. Client110 may be a remote computer with a display or may even be a mobiledevice configured with an application to send or receive information,such as to receive a charge condition of the energy management system124 or components thereof. Dashboard 112 may be located in the home 134and may be configured to send or receive any of the informationdiscussed herein. A data processing system, such as server 104 or server106, or clients (client 110, dashboard 112) may contain data and mayhave software applications or software tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers andclients are only examples and do not to imply a limitation to aclient-server architecture. As another example, an embodiment can bedistributed across several data processing systems and a data network asshown, whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems (server 104, server 106, client 110, dashboard 112)also represent example nodes in a cluster, partitions, and otherconfigurations suitable for implementing an embodiment.

The energy management system 124 may comprise one or more applicationspecific hardware 114 that may electrically couple an energy source orelectrical load to a universal energy flow manager 126. The architecturemay allow homes to not have to entirely depend on utility to supply allEV and other load demand via level 2 AC charges. Due to the design ofthe universal energy flow manager and connections interface, asdiscussed herein, a home 134 may be provided with the ability to setcharging targets to multiple EVs. This may be achieved concurrently orby setting a priority level for different loads. The power available maybe limited by the energy sources available rather than by an existingspecifications of home circuit. The energy management system 124 mayharvest energy from, for example, the utility grid, energy storage,solar photovoltaic panels, wind energy, fuel cells and EVs in garage. Inan aspect, when available energy is to be judiciously used, the systemmay monitor home loads and redirect maximum AC power to charge an EVhaving a highest priority. Further, all EVs at home may have access toDC Fast Charging rather than swapping cars to be close to one-port DCcharger. Even further, home energy may be scalable wherein by connectinganother system in parallel, power may be doubled.

Application 136, Client application 120, dashboard application 122, orany other application such as server application 116 may implement anembodiment described herein. Any of the applications may use data fromthe universal energy flow manager 126, energy sources or loads tocompute power or energy requirements. The applications may also obtaindata from storage unit 108 for predictive analytics. The applicationscan also execute in any of data processing systems (server 104 or server106, client 110, dashboard 112).

Server 104, server 106, storage unit 108, client 110, dashboard 112, maycouple to network/communication infrastructure 102 using wiredconnections, wireless communication protocols, or other suitable dataconnectivity.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to client 110, anddashboard 112. Client 110, and dashboard 112 may be clients to server104 in this example. Client 110 and dashboard 112 or some combinationthereof, may include their own data, boot files, operating systemimages, and applications. energy management environment 100 may includeadditional servers, clients, and other devices that are not shown.

Server 106 may include a search engine configured to search information,such as weather condition, grid consumption data, power outagehistorical data, total energy available from an energy source, arequired charging duration, user preferences, user feedback, orotherwise other energy management system data, automatically or inresponse to a request from an operator for power delivery as describedherein with respect to various embodiments.

In the depicted example, network/communication infrastructure 102 mayinclude the Internet. Network/communication infrastructure 102 mayrepresent a collection of networks and gateways that use theTransmission Control Protocol/Internet Protocol (TCP/IP), ControllerArea Network BUS (CAN bus), local area network (LAN), wide area network(WAN) and/or other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,residential, educational, and other computer systems that route data andmessages. Of course, energy management environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet. FIG. 1 is intended as an example, and not as anarchitectural limitation for the different illustrative embodiments.

Among other uses, energy management environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system.Energy management environment 100 may also employ a service-orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Energy management environment 100 may also employ a cloud computingmodel of service delivery for enabling convenient, on-demand networkaccess to a shared pool of configurable computing resources (e.g.,networks, network bandwidth, servers, processing, memory, storage,applications, virtual machines, and services) that can be rapidlyprovisioned and released with minimal management effort or interactionwith a provider of the service.

With reference to FIG. 2 , this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such client 110,dashboard 112, s server 104, or server 106, in FIG. 1 , or another typeof device in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is described as a computer only as anexample, without being limited thereto. Implementations in the form ofother devices, may modify data processing system 200, such as by addinga touch interface, and even eliminate certain depicted components fromdata processing system 200 without departing from the generaldescription of the operations and functions of data processing system200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to North Bridge and memory controller hub (NB/MCH)202 through an accelerated graphics port (AGP) in certainimplementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Audio adapter 216, keyboard and mouse adapter 220, modem 222, read onlymemory (ROM) 224, universal serial bus (USB) and other ports 232, andPCI/PCIe devices 234 are coupled to South Bridge and input/output (I/O)controller hub (SB/ICH) 204 through bus 218. Hard disk drive (HDD) orsolid-state drive (SSD) 226 a and CD-ROM 230 are coupled to South Bridgeand input/output (I/O) controller hub (SB/ICH) 204 through bus 228.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. Read only memory (ROM) 224 may be, forexample, a flash binary input/output system (BIOS). Hard disk drive(HDD) or solid-state drive (SSD) 226 a and CD-ROM 230 may use, forexample, an integrated drive electronics (IDE), serial advancedtechnology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and input/output (I/O) controller hub(SB/ICH) 204 through bus 218.

Memories, such as main memory 208, read only memory (ROM) 224, or flashmemory (not shown), are some examples of computer usable storagedevices. Hard disk drive (HDD) or solid-state drive (SSD) 226 a, CD-ROM230, and other similarly usable devices are some examples of computerusable storage devices including a computer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2 . The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 116 and clientapplication 120 are located on storage devices, such as in the form ofcodes 226 b on Hard disk drive (HDD) or solid-state drive (SSD) 226 a,and may be loaded into at least one of one or more memories, such asmain memory 208, for execution by processing unit 206. The processes ofthe illustrative embodiments may be performed by processing unit 206using computer implemented instructions, which may be located in amemory, such as, for example, main memory 208, read only memory (ROM)224, or in one or more peripheral devices.

Furthermore, in one case, code 226 b may be downloaded over network 214a from remote system 214 b, where similar code 214 c is stored on astorage device 214 d in another case, code 226 b may be downloaded overnetwork 214 a to remote system 214 b, where downloaded code 214 c isstored on a storage device 214 d.

The hardware in FIG. 1 and FIG. 2 may vary depending on theimplementation. Other internal hardware or peripheral devices, such asflash memory, equivalent non-volatile memory, or optical disk drives andthe like, may be used in addition to or in place of the hardwaredepicted in FIG. 1 and FIG. 2 . In addition, the processes of theillustrative embodiments may be applied to a multiprocessor dataprocessing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub (NB/MCH) 202. A processing unitmay include one or more processors or CPUs.

The depicted examples in FIG. 1 and FIG. 2 and above-described examplesare not meant to imply architectural limitations. For example, dataprocessing system 200 also may be a tablet computer, laptop computer, ortelephone device in addition to taking the form of a mobile or wearabledevice.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and Hard disk drive (HDD) or solid-state drive(SSD) 226 a is manifested as a virtualized instance of all or someportion of Hard disk drive (HDD) or solid-state drive (SSD) 226 a thatmay be available in the host data processing system. The host dataprocessing system in such cases is represented by data processing system200.

Energy Management System

Turning now to FIG. 3 , a universal energy flow manager is shown. Theuniversal energy flow manager 126 may be a part of the energy managementsystem 124 and may comprise a housing 304, an energy storage device 306(battery) disposed in the housing and configured to provide on-boardenergy, a power electronics module 308 disposed in the housing andconfigured to convert and manage power, a connections interface 312configured to receive external application specific hardware 114, and adistribution and communications module 310 that comprises at least aHVDC Bus (High-Voltage Direct Current Bus) having variable power limitsand configured to power an entire load requirement of one or morecoupled electrical loads up to a defined power limit determined by anaggregation of power from one or more coupled energy sources. The one ormore coupled electrical loads and the one or more coupled energy sourcesare external to the universal energy flow manager and connect to theHVDC Bus or LVDC Bus through respective application specific hardware114 and the connections interface 312. A plurality of energy sources andloads may be connected to a universal energy flow manager and aplurality of universal energy flow managers 126 may be connected inparallel as shown in FIG. 4A or in series as shown in FIG. 4B. It thenfollows that; various combinations may be obtained to supply an entireload requirement.

The universal energy flow manager 126 may also comprise an application136 comprising control software configured to communicate with and/orcontrol external devices that supply or receive power from the energyflow manager.

The universal energy flow manager 126 may also comprise a thermalmanagement component 314 configured to regulate a temperature of theuniversal energy flow manager 126 by heat exchange through one or moreheat exchange processes including, for example, a gas or liquid medium.

The power electronics module 308 may comprise components forbi-directional high voltage conversion, bi-directional low voltageconversion and electronics for energy storage management. As shown inFIG. 5 , a DC-DC converter such as the bi-directional DC-DC converter520 may be disposed between the HVDC Bus and the energy storage deviceand configured to convert power between the energy storage device andthe HVDC Bus. Herein, for example, a 48V output of the energy storagedevice may be converted into a 400-900V input for the HVDC Bus and viceversa. Another DC-DC converter such as another bi-directional DC-DCconverter 522 may be disposed between a LVDC Bus 514 (Low-Voltage DirectCurrent Bus) and the energy storage device 306 and configured to convertpower between the energy storage device and the LVDC Bus. Thus, a 48Voutput of the energy storage device may be converted to a 12-48V inputfor the LVDC Bus and vice-versa. This may allow stack/energy flowmanager balancing when charging or discharging and may further serve asan additional low voltage power source for external loads. The energystorage device 306 may be a backup storage and may be used to charge avehicle. Further, its presence in the universal energy flow manager 126may allow the universal energy flow manager 126 to be used as single ormultiple vehicle 12V or 24V or 48V battery for long term storage. Thepower electronics module may comprise one or more controllers which maymonitor a state of the HVDC Bus and LVDC Bus. The power electronicsmodule may further rapidly adjust the HVDC Bus voltage level to match aparticular EV. By virtue of single or a plurality of interleavedconverters, flexible power limits that may be adjustable to allow a fullrange of power from hundreds of mW to full power may be designed forimproved load efficiency. The flexible power limits may be made possibleby the ability to automatically couple a plurality of ASHs together aswell as an ability to automatically detect and adjust a time-divisionenergy multiplexing operation to manage power flow and conversion asdiscussed herein. Integrated protection for overvoltage, undervoltage,over temperature and under temperature may also be implemented. Toensure proper isolation, the energy management system may performautomatic self-checking by applying safe voltage level output line toensure proper isolation. Continuous monitoring of HVDC signals may beperformed with reference to earth ground to flag fast or slow drifts.Further, sensors may be integrated into the energy flow manager toprovide automatic warnings active load control during hazardousconditions. These may include shutting down a main water control valve,shutting down individual circuit breakers, purging air control duringcarbon monoxide detection or during indoor air quality warning, sensingair quality, detecting smoke and fire, and measuring temperature onbattery stacks, PCBs, and connectors. Further the HVDC may be run with afrequency flyback converter.

The power electronics may further include a battery management system(BMS) configured to perform active measurements and cell balancing,charge and discharge control, temperature measurement, and safety logicwith redundant sensing and supplies.

In an aspect, the distribution and communications module 310 may furthercomprise components for DC distribution with flexible power limits, acommunication hub for energy management system 124 comprising a masterdigital controller (not shown), components for voltage and currentmeasurement, monitoring, and protection, and a disconnect unit(contactors) with faulted contactor detection configured to disconnectthe distribution and communications module 310 from an applicationspecific hardware 114. The distribution and communications module 310may also comprise components for DC bus pre-charging and controlelectronics for full system integration. For example, a pre-chargecircuit may be controlled by a controller of the HVDC Bus to match thevoltage on both sides of contactors 512 prior to closing of thecontactors 512. Further, with regards to components for voltage andcurrent measurement, the universal energy flow manager receiveinformation about the voltage or power needed in an ASH viacommunication with the ASH. For example, in case of an EV ASH, the EVmay command current and voltage from a charger, which may be transmittedto the ASH and then to the energy flow manager. In other cases, theenergy flow manager may use internal measurements to ensure operationdoes not exceed design ratings.

The housing 304 may comprise a structure that may encloses othercomponents of the universal energy flow manager 126. The housing mayhave HVIL (high-voltage interlock loop) and safety features as well as“open case” detection which may cut off power supply upon opening ofsaid housing. Leakage current measurements may be performed in a GFCI(Ground-Fault Circuit Interrupter) implementation, and an HVDC voltagedetection may recognize increased voltage drops that may indicatetempering with connections. Backup isolation measurement may beimplemented in the universal energy flow manager 126 and/or each ASH. Anapplication may collect feedback from all ASHs and may perform short tochassis or leakage detection via individual HDVC (Positive and negative)measurements. The application may also perform comparison of HVDCmeasurements from all networked HW. The application may run in aplurality of controllers as a backup and voting logic may be implementedto ensure that a safe state can always be reached.

The housing may also comprise a quick connect 316 that may enableconnection of one universal energy flow manager 126 to another viaparallel and series configurations. In an embodiment, the housing mayhave a display such as a touchscreen display or dashboard 112 configuredto enable operator input. In an illustrative embodiment, a display ofthe energy flow manager may receive load shedding configuration form auser and provide power management of energy sources based on the userconfiguration. Further, the display may output power flow analysesrepresentative of a state of energy sources and/or electrical loads.

Connected to high voltage DC Bus 516 of the universal energy flowmanager 126, through the connections interface 312 and contactors 512,may be one or more application specific hardware 114. The applicationspecific hardware may comprise, as shown in FIG. 5 , applicationspecific hardware controller (ASH controller 510) which may be receiveinstructions from the universal energy flow manager. The ASH may alsocomprise a power conversion module 502 configured to convert, responsiveto instructions from the ASH controller, an electrical energy from afirst input form into a defined second output form. The power conversionmodule 502 may be optional. It may also comprise contactors that areoperated to connect an input to an output. The defined second outputform may be a form that is requested by the universal energy flowmanager 126. For, example, the high voltage DC Bus 516 may accept 400Vand thus the ASH dedicated to solar input may be instructed to convert a48V input into a 400V output for the high voltage DC Bus 516.

An ASH may be configured to operate in a first operation mode as asource ASH in which the ASH may connect an energy source to theconnections interface and thus, provide energy to the universal energyflow manager. The ASH may also operate in a second operation mode as aload ASH in which the ASH connects a load to the connections interfaceand thus, receives energy from the universal energy flow manager fordelivery to the load.

An energy management system may comprise a plurality of ASHs. Forexample, an ASH may be an EV ASH 604 (FIG. 6 ), an EV ASH with DC Fastcharging 902 (FIG. 9 ) or a home ASH 704 (FIG. 7 ). An ASH may also be arenewable energy source ASH such as a PV/Wind ASH 906 (photovoltaic/windASH). It may also be a fuel cell ASH 910 or hydrogen reformer ASH 912.Further, based on the design of the power conversion module 502 of theASH (which may include a plurality of different converters) one or moredifferent types of sources may be connected to the same ASH as shown inFIG. 8 . The ASH may further comprise Isolation detection and protectioncomponents, and interface connector and housekeeping power supplies(HKPS, not shown).

In an embodiment, the ASH may be configured to automatically connect anyenergy source or any electrical load to the universal energy flowmanager.

In the home ASH of FIG. 7 , the home ASH 704 may to provide power, inthe first operation mode, from a grid energy source (power grid 128) tothe universal energy flow manager 126. This energy may be provided to aload device 508 through a load ASH. The energy may be provided as is orconverted to another form (such as to a lower voltage) or combined withanother source in any suitable configuration of one or more coupleduniversal energy flow managers 126 and provided to the load ASH 506. Thehome ASH 704 may alternatively receive power from the universal energyflow manager, in the second operation mode, for provision to a homeappliance or tool or for sale to the grid/utility. This home applianceor tool may receive the energy via a smart circuit breaker panel 922 incommunication with the universal energy flow manager 126 and inelectrical connection with the home ASH 704 working as a load ASH. Insome cases, the smart circuit breaker panel 922 may be configured as anASH having a direct connection to the HVDC Bus.

In an illustrative embodiment, the energy flow manager may be configuredto operate one or more ASHs in their first operation modes to aggregateenergy to Direct Current (DC) fast charge one or more electric vehiclesor one or more electrical loads, wherein DC fast charging may includeLevel 3 DC fast charging or in some cases charging specifications of400V-900V. For electric vehicles, the charging speed provided for400V-900V may be 3 to 20 miles per minute. Due to the ability toaggregate a plurality of energy sources through corresponding ASHs whichare external to the universal energy flow manager 126 and which arefurther managed locally by respective ASH controllers 510 as opposed todirect management via an architecture of the universal energy flowmanager 126, any load requirement may be met as long as energy sourcescan be aggregated. Thus, there may not be any need to redesign theuniversal energy flow manager 126 for new types of energy sources.

In an embodiment wherein DC fast charging is utilized, a plurality ofelectric vehicles or electrical loads may be DC fast chargedconcurrently. In another embodiment, the home ASH 704 may utilize aplurality of universal energy flow manager 126 to charge electricvehicles in parallel or in a time multiplexed method. The home ASH 704may allow EV charging from not only a grid but also from other sourcesincluding, solar, wind, fuel cell, another EV, grid battery or acombination of these through connection of the other sources to the homeASH 704. The home ASH 704 may also have a standard 240V plug 904 and/or120V plug. A power conversion module 502 of the home ASH 704 maycomprise an inverter or rectifier to convert DC to AC or AC to DCrespectively depending on the mode of operation. It may also comprise aDC to DC converter to supply DC loads.

In an electric vehicle ASH (EV ASH 604), as shown in FIG. 6 , the EV ASH604 may be configured to send power, in the first operation mode, froman electric vehicle 132 energy source (for example, an EV battery) tothe universal energy flow manager. This energy may be routed to a loaddevice 508 through a corresponding load ASH 506. Thus, the electricvehicle 132 may power a, for example, a home or another EV. In thesecond operation mode of the EV ASH, the electric vehicle 132 may be aload and may receive power from the universal energy flow manager viathe EV ASH 604. Thus, another device (such as the grid, renewable energysource or another EV) may power the subject electric vehicle 132.

The ASH controller 510 of the EV ASH 604 may be an electric vehiclecommunication controller (EVCC) or may interface with an EVCC. The EVCCmay have inbuilt CAN and PLC communication protocols and may serve as acommunication gateway between the vehicle and the rest of the energymanagement system 124. The power conversion module 502 may comprise aDC-DC converter configured to convert a first input voltage to a secondoutput voltage. Contactors of the EV ASH 604 may be used to implement atime multiplexed charging algorithm and for protection during isolationfailure detection. In a vehicle to vehicle implementation where the EVASH 604 provides energy from one vehicle to another, EV ASHs maycommunicate with the universal flow manager to set the proper HVDC busvoltage, wherein, for example, a 400V EV may supply an 800V EV. This mayallow for power transfer from vehicle to vehicle.

The ASH may further be a renewable energy source ASH or fuel cell ASHconfigured to provide power, in the first operation mode, from arenewable energy source or fuel cell to the universal energy flowmanager, where the renewable energy source is a photovoltaic/solarenergy source, a wind energy source, or another renewable energy source.As shown in FIG. 9 , the ASH may also be a PV/Wind ASH 906(photovoltaic/wind ASH), a fuel cell ASH 910, or a hydrogen reformer ASH912.

In an aspect herein, the energy management system may universal energyflow manager 126 may be portable and may act as a standalone energysource by using the on-board energy storage device 306 as a battery tocharge one or more loads. For example, the universal energy flow manager126 may be used as a range extender battery in an electric vehicle powersupply system.

Turning now to FIG. 9 and FIG. 10 , an energy management environment 100and a method thereof are shown. The environment 100 may comprise anetwork/communication infrastructure 102, a plurality of universalenergy flow managers 126, a renewable energy source 130, a plurality ofelectric vehicles 132, a home ASH 704, a plurality of EV ASH with DCFast charging 902, a 240V plug 904 of the home ASH 704, a PV/Wind ASH906, a DC Bus 908, a fuel cell ASH 910, a hydrogen reformer ASH 912, aservice entrance 914, an AC line 916, a DCDC converter 918, a DC line920, and a smart circuit breaker panel 922. In the environment, theplurality of electric vehicles 132 may be charged concurrently. Theplurality of energy sources may also be harvested via connections to oneor more universal energy flow managers 126. An application 136 of theone or more universal energy flow managers 126 may perform an energymanagement process 1000 of FIG. 10 . The energy management process maybegin at step 1002 wherein the process provides the one or moreuniversal energy flow managers. In the case of a plurality of universalenergy flow managers, a controller of one primary manager (primarycontroller) may control a controller of the remaining secondary managers(secondary controllers). Herein, the primary controller may determinewhere to route energy and may route said energy to a DC Bus/HVDC Bus ofa connected secondary manager for provision to one or more loads. Thus,the primary controller may detect or measure an entire load requirementof one or more coupled electrical loads in step 1004. This may be donethrough detecting that one or more ASHs are connected. The controllermay combine, in step 1006, energy from one or more coupled energysources coupled to the energy flow manager through instructions relayedto respective ASH controllers of the one or more coupled energy sources.The controller may then power, in step 1008, through the ASHs, theentire load requirement of the one or more coupled electrical loads upto a defined power limit determined by said aggregating. The poweringmay further conform to a plurality of defined logic related toperforming an EV charging, performing a backup charging, reducing costs,maximizing battery life and allowing off grid usage. As shown in step1010, this may be achieved by operating a first ASH in a first operationmode as a source ASH to connect an energy source of the one or morecoupled energy sources to the connections interface of the universalenergy flow manager 126 to provide energy to the universal energy flowmanager. The powering of the entire load requirements may also beachieved, as shown in step 1012, through operating a second ASH in asecond operation mode as a load ASH to connect the one or more coupledelectrical loads to the connections interface to receive energy from theuniversal energy flow manager. By so doing, the energy flow manager mayroute power from EV 1 to charge EV 2 as shown in FIG. 9 . The energyflow manager may also route power from the renewable energy source 130to charge EV3. EV 4 may be used as a source to power the home load viathe home ASH 704 acting as a load ASH. Further, these may all happenconcurrently as the available power may not be limited by just theavailable grid energy from the service entrance 914.

FIG. 11 illustrates an energy management in a residential setting havinga home 134 with a plurality of EV ASHs with DC Fast charging 902. Autility underground feeder 1104 may provide currents of, for example,200 A to a meter 1102 of the home 134. Power may flow bi-directionallybetween the meter 1102 of the home 134, which is connected to theuniversal energy flow manager 126, and the utility underground feeder1104. The universal energy flow manager 126 of the home may also beconnected to a plurality of other energy sources (130, 128). Threeelectric vehicles 132, for example, may be connected to the universalenergy flow manager 126. Herein, each of the three electric vehicles 132may have an EV ASH with DC Fast charging 902. To charge the electricvehicles 132 in a selected time period of about 1.5 Hours, 75 kWh ofenergy might be needed, assuming an EV consume power of 50 kW. Whenthree vehicles EV 5, EV 6 and EV 7 are being charged concurrently, acurrent of, for example, 125 A each may be needed for the electricvehicles 132. Since the utility underground feeder 1104 may be limitedto 200 Amperes, and 70 Amperes may be needed for home loads, theremaining current needed for DC fast charging may be drawn from theother energy sources to DC fast charge the vehicles concurrently. Thuslevel 3 charging of one or more vehicles may be introduced to aresidential setting without compromising the ability to concurrentlyprovide energy to other home electrical loads.

On the other hand, another home 1110 may not have the universal energyflow manager 126 and may thus be limited to the 200 Amperes current fromthe utility underground feeder 1104. Said another home 1110 may have ACLevel 2 chargers (AC L2 chargers 1106) and AC Level 1 chargers (AC L1chargers 1108). An AC charger may provide power to the on-board chargerof a vehicle, converting that AC power to DC for the battery. Theacceptance rate of the on-board charger may vary but conventionally, itmay four or five hours to over twelve hours to fully charge at Level 2.Thus, in said another home 1110, wherein 70 Amperes may be needed topower home loads, the remaining 130 Amperes may be divided between theelectric vehicles 132, wherein charging may be limited to only Level 2charging at 50 Amperes and Level 1 charging at 30 Amperes. Thus EV 8 andEV 9 may be fully charged in 6.25 Hours, for example, and EV 10 may befully charged in 22 Hours.

Intelligent Energy Management

The illustrative embodiments further recognize that conventionalresidential energy management systems may mostly interface with the gridfor energy delivery and may, at best, rely on predefined logic forenergy management. The illustrative embodiments recognize thatconventional systems may be incapable of predicting energy consumptionneeds and restricted to rationing energy due to underlying architecturethat may not allow variable sources of energy to be used. Theillustrative embodiments recognize that while energy and power needs maybe estimated to prepare for incoming energy, this may be largely errorprone and may not account for external influencing factors such as poweroutages, unexpected variations in load, changing weather/environmentalconditions. Moreover, conventional systems may, at best, rely onconnecting energy sources having predetermined and matching voltage andpower specifications. The illustrative embodiments recognize that thismay limit the control an operator may have over a power managementsystem, leading to imprecise management.

As far as power metering of individual sources and loads of an energystorage system, presently, conventional energy management systems maycharge and discharge all individual modules together without being ableto provide access to detailed statistics about the individual sourcesand loads. The illustrative embodiments recognize that monitoring theenergies of individual modules in a larger system and controlling themindividually to ensure the efficiency and safety of the system as awhole may be critical. For example, by being able to safely connect anddisconnect individual loads, based on analyses of detailed informationabout the system as a whole, without being restricted to predeterminedload specifications, the energy management system may be made moreefficient, modular and scalable and the availability of energy fordifferent predictive operations may be significantly increased.

The illustrative embodiments used to describe the disclosure generallyaddress and solve the above-described issues and other relatedcomplications by intelligent proposal of power delivery operations thatmay enhance utility cost, available back up energy, charging time andbattery life. The illustrative embodiments may solve these problems in apreparatory or “forward-thinking” process that anticipates not only thepower/energy demands of home loads, electric vehicles, and other loadappliances but also anticipate the availability of energy from connectedrenewable energy sources, fuel cells, EV batteries and/or other energysources and operates to meet said load demands by exploiting the modulararchitecture of the universal energy flow manager 126 and connectedASHs.

Concerning intelligent proposals, certain operations are described asoccurring at a certain component or location in an embodiment. Suchlocality of operations is not intended to be limiting on theillustrative embodiments. Any operation described herein as occurring ator performed by a particular component, e.g., a predictive analysis ofload and source data and/or a natural language processing (NLP) analysisof contextual calendar or weather data, can be implemented in such amanner that one component-specific function causes an operation to occuror be performed at another component, e.g., at a local or remote machinelearning (ML) or NLP engine respectively.

An embodiment performs automatic power flow routing from multiplesources like multiple batteries, grid, solar, wind fuel cell, other EVsto multiple loads. Another embodiment enables detailed power metering ofall sources and loads. A further embodiment performs intelligent loadshedding to increase home backup time. Yet another embodiment allows fordirect billing to utility to eliminate the need for additional meters ifa meter s required to be installed by a utility company.

In an aspect, one embodiment monitors and manages cumulative energy ofthe energy management system. The embodiment may make decisions on whichsource to extract energy from based on attributes such as minimizing EVcharging times, maximizing available backup during outages, and thelike. Another embodiment may monitor a variety of profile sourcesconfigured for operators and environments of the energy managementsystem, for example, a renewable energy storage plant that may need touse or store energy during high renewable energy production times, or anelectric vehicle may require DC fast charging. A profile source may bean electronic data source from which information usable to determine aprofile characteristic of the consumer can be obtained. For example, aprofile source may be an operator profile 1222 providing operatorinformation such as preference configuration, including for example, anorder of importance of loads to charge, a calendar applicationcomprising the operator's historical and activities and associatedenergy demands, energy generation events, feedback from the operator orgroup of operators, or otherwise other operator data. A profile sourcemay also be environment profile 1228 providing data such as past weatherconditions, predicted future weather conditions, historical power outagedata or otherwise other environmental data. A profile source may be adevice, apparatus, software or a platform that may provide informationfrom which an energy/power delivery or retrieval characteristic may bederived. For example, a dashboard 112 may operate as a profile sourcewithin the scope of the illustrative embodiments. Moreover, a communitysuch as a group of operators in a residential facility or a group ofoperators in a group or residential facilities may be a profile sourcewherein a plurality of storage and delivery characteristics may beobtained to derive a preference, liking, sentiment, or usage of energy.Further, measured power, voltage and health metrics or parameters aboutsources and loads of the universal energy flow manager 126 and energymanagement system 124 as a whole, referred to herein generally as energymanagement system parameters 1220 may be input data and may be utilizedto learn from and derive patterns for providing and retrieving energy inthe energy management system 124. An operator's/environment's profiledata, information and preference, are terms that are used hereininterchangeably to indicate a constraint of one or moreusers/environments that may affect power/energy routing.

Furthermore information/data about the components of the energymanagement system 124 (such as voltage, power, current, number ofconnected sources and loads, temperature, state of health (SOH) ofbatteries, state of charge (SOC) of batteries, average energyconsumption, vehicle energy demand profile, home energy demand profile,and the like or otherwise other energy management system parameters 1220may form part of or be separate from the constraints and may be obtainedfor use as input to an intelligent power delivery module 1216 forpredictive analytics as described hereinafter. Thus, the profile source1224 information and energy management system parameters 1220 maycollectively form at least a part of the input data 1202 or constraintsfor the intelligent power delivery module 1216 to predict optimal powerdelivery operations and schedules and perform said operations andobserve said schedules.

Operating with profile information from one or more profile sources, anembodiment routinely evaluates the constraints that are applicable tothe system and operators. The embodiment adds new constraints/input datawhen found in profile information analysis, modifies existingconstraints when justified by the profile information analysis, anddiminishes the use of past constraints depending on the feedback, theobserved usage of the constraint and/or presence of support for the pastconstraint in the profile information. A past constraint may bediminished or aged by deprioritizing the constraint by some degree,including removal/deletion/or rendering ineffective the past constraint.More generally, profile information may be obtained from any sourceavailable to the energy management system 124.

The input data 1202 as determined by an embodiment may be variable overtime. For example, loads may have time varying parameters that may bemeasured and used as input. This may provide real time proposals forefficiently operating the energy management system 124. Similarly, thegrid energy source may indicate an outage. However, energy may still beneeded. Thus, the power delivery module 1216 may propose an option toroute energy from a solar panel through a solar panel ASH to power aload in a home.

Further, based on predictive analytics about inclement weather orcertain seasons of the year during which renewable energy sources maynot be readily available, the power delivery module 1216 may proposedecreasing the use of certain home loads to meet a predicted DC fastcharging demand and vice versa. Herein, the ability to connect otherenergy sources, in addition to the ability to automatically detect thepower/energy metrics of a connected energy source or load may curb powersharing limitations typically observed with systems that require energysources with particular specifications.

The intelligent power delivery proposals and techniques described hereingenerally are unavailable in the conventional methods in thetechnological field of endeavor pertaining to residential energymanagement systems. A method of an embodiment described herein, whenimplemented to execute on a device or data processing system, comprisessubstantial advancement of the functionality of that device or dataprocessing system in proposals by obtaining constraints and using anadvanced modular universal energy flow manager architecture.

In further embodiments, a machine learning engine may be provided toincrease the resolution and efficacy of predictions made by theuniversal energy flow manager based on a comparison of sensed andreceived information. The machine learning engine may detect patternsand weigh the probable outcomes and energy demand profiles based onthese patterns. As an operator engages with the universal energy flowmanager 126, data regarding the consumption may be collected and storedfor analysis by the controller of the universal energy flow manager 126or another network-connected computerized device. The data may beaggregated to allow additional resolution in detecting patterns andpredicting behavior. The machine learning engine may perform an analysison time series data gathered at the source or load or environment, andsupplemental information such as that provided over a network, and/orother information to draw correlations. For example, the machinelearning engine may perform a linear algebra regression analysis on thetime series step data to find the best-fit parameter values. The machinelearning engine may additionally return operational parameters, forexample, that may be used by a controller in energy management.

Client application 120 of FIG. 1 , dashboard application of FIG. 1 ,server application 116 of FIG. 1 or any other application suchapplication 1204 may implements an embodiment described herein. Any ofthe applications can use data from the energy management system 124, andprofile sources to propose power delivery proposals and implement saidproposals. The applications can also obtain data from storage unit 108for predictive analytics. The applications can also execute in any dataprocessing systems (server 104 or server 106, client 110, dashboard112).

In one aspect, a computer-implemented method is disclosed that comprisesreceiving an energy demand state of one or more coupled electrical loadscoupled to a universal energy flow manager via a load applicationspecific hardware (load ASH), the energy demand state being indicativeof a desired amount of energy needed by the one or more coupledelectrical loads in an energy management environment. The method mayfurther comprise receiving an available energy state of one or morecoupled energy sources coupled to the universal energy flow manager viaa source application specific hardware (source ASH), the availableenergy state being indicative of an available amount of energy from theone or more coupled energy sources in the energy management environment,generating input data using at least the energy demand and availableenergy states, extracting one or more features from the input data, theone or more features representative of a characteristic of a request fora power delivery proposal operation, proposing, using the power deliverymodule, at least one power delivery proposal for the one or more coupledelectrical loads, and performing a power delivery operation based on thepower delivery proposal.

Concerning FIG. 12 , this figure depicts a diagram of an exampleconfiguration for intelligent power delivery in accordance with anillustrative embodiment. The intelligent power delivery can beimplemented using application 1204 in FIG. 12 . Application 1204 may bean example of application 136, server application 116, clientapplication 120 or dashboard application 122, for example. Theapplication 1204 may receive or monitors, for example in real time, aset of input data 1202. The input data comprises energy managementsystem parameters 1220. The input data may also comprise operator andenvironmental characteristics from profile sources 1224 (operatorprofile 1222, environment profile 1228) such as preferences, pre-plannedenergy storage, average daily driving distance, past driving energyconsumption per mile, calendar data for a timelining procedure, andweather data.

In one or more embodiments described herein, characteristics,properties, and/or preferences associated with an operator, anenvironment, a load, a source etc. are referred to as “features”. In oneor more embodiments, the configuration 1200 defines and configures analgorithm and/or rule to drive feature selection results. In particularembodiments an algorithm may include, for example, determining a lowestcommon value for a feature, and determining whether the value satisfiesa best match within a threshold value (e.g., 90%) of the feature. In anembodiment, the system may prioritize certain features so that featuressuch as battery life, charging time, utility cost and available backupenergy carry different weights. In an embodiment, after a commondenominator in a plurality of operators is found, the configuration 1200understands the problems with individual operators, and extracts andderives the best feature values that will help in intelligent proposals.

In an embodiment, features may be selected or extracted from outside themachine learning model. However, in another embodiment, features mayadditionally be extracted inside the machine learning model/deep neuralnetwork and thus may be integral to the model. Feature extraction andselection are therefore generally used interchangeably herein.

In an embodiment, feature selection/extraction component 1214 isconfigured to generate relevant features, based on contents of a requestfrom application 1204, using data from all the different availablefeatures (e.g., energy management system parameters 1220, operatorprofile 1222, environment profile 1228). In the embodiment, featureselection/extraction component 1214 may receive a request fromapplication 1204 which may include at least an identification ordetection of a load as well as instructions to propose and implementcorresponding power delivery proposals. Using the energy managementsystem parameters 1220 and/or profile source 1224, featureselection/extraction component 1214 may obtain a combination of specificenergy management system parameters 1220, profile information fromoperator profile 1222, and environmental data from environment profile1228. In the embodiment, feature selection/extraction component 1214 mayuse a defined algorithm of prioritization to generate the features asfeature profile. In a particular embodiment, the feature profileincludes each feature (e.g., 1. a load voltage, 2. a load power, 3. aweather forecast, 4. connected secondary energy flow managers 5. asource power, 6. A source power 7. an EV charging time, 8. an operatorpreference, 9. remaining life cycles of an EV battery and 10. weightsgiven to each feature). Using the extracted features and a trained M/Lmodel 1206 that has been trained using a large number of differentdatasets, power delivery module 1216 may determine a power deliveryproposal 1212 for the system.

The power delivery proposal 1212 may comprise instructions toautomatically route a defined amount of power from a particular sourceto a particular load. The automatic power flow routing may be frommultiple sources including, for example, multiple batteries, the grid,solar panels, wind energy source, fuel cells, and other EVs to multipleloads such as a home load or an EV with depleted battery energy near atime of regular usage.

The power delivery proposal 1212 may comprise detailed reports aboutenergy consumption that may enable a utility company to understand howmuch energy is used for EVs or other specific loads, which may eliminateor reduce the need for separate meters and thus, reduce utility costs.

The power delivery proposal 1212 may also comprise instructions toautomatically route power to specific loads based on user preferences.For example, a user preference may include minimizing energy transferfrom or to a battery pack. In an embodiment, the power delivery proposal1212 may comprise instructions to maximize an EV charging speed. Hereina connected EV ASH 604 may be operated to close its contactors.Unnecessary loads such as AC and pool equipment may be shut down, gridpower usage may be maximized, power from renewable sources may bemaximized and routed to the high voltage DC Bus 516 and power from thehigh voltage DC Bus 516 may be delivered to the EV through the EV ASH604.

The power delivery proposal 1212 may further comprise instructions tooperate the energy management system 124 in an intelligent backup modeof operation wherein for a defined period of time after a grid poweroutage (for example 3 hours), home loads may be operated. After saiddefined period of time, non-essential loads may be shut down based onoperator preferences or learned shutdown parameters and remaining energysources may be used to automatically and progressively manage thesystem. Thus, the power delivery proposal may include load sheddinginstructions to increase home backup time. Further, the system or theenergy flow manager may have a power inverter that provides seamlesspower transfer. The power inverter may be configured to be running atall times and may switch power within one cycle. The power deliveryproposal 1212 may comprise intelligent energy forecasting that mayestimate a required amount of energy to be stored overnight andcompensate for changing weather conditions that may hinder photovoltaicand wind energy availability. Further an automatic grid outage interfaceto the utility may allow the energy management system to anticipaterequired dropout time.

The power delivery proposal 1212 may further comprise instructions tomaximize pay back and usage of renewable energy sources during a definedtime period, such as, during the day. For example, excess power may besold back to utility or stored in the energy storage device 306 of theuniversal energy flow manager 126. At night or during off peak hours,renewable energy may be used to power home loads and charge batteries.Power may also be obtained from an EV while retaining a minimum chargedesired by an EV operator. One or more of these strategies may be chosento minimize or eliminate peak charge rates. Further instructions maycomprise optimizing system management to achieve net zero or near netzero utility energy transfer.

Even further, the power delivery proposal 1212 may comprise instructionsto maximize battery life wherein for operators interested in a lowestmaintenance cost, battery usage may be minimized to achieve relativelylonger battery life.

The power delivery proposal 1212 may further comprise instructions torebalance energy storage between batteries or energy storage devices 306of connected universal energy flow managers 126 by activecharge/discharge throttling.

The proposals may be provided in real time as the input changes andimplementation of the proposals may be performed in real time or uponreceiving operator confirmation. User feedback concerning an accuracy ofthe proposals may also be used in modifying the machine learning model.By providing one or more of these cell operation and manufacturingoperation proposals, and executing said proposals, a highly energyefficient, self-supporting and cost-efficient energy management systemand environment may be obtained. These examples are not meant to belimiting and any combination of these and other example power outputproposals are possible in light of the descriptions.

The power delivery module 1216 can be based, for example, on a neuralnetwork such as a recurrent neural network (RNN), although it is notmeant to be limiting. An RNN is a type of artificial neural networkdesigned to recognize patterns in sequences of data, such as numericaltimes series prediction and numerical time series anomaly detectionusing data emanating from sensors, generating image descriptions andcontent summarization. RNNs may use recurrent connections (going in theopposite direction that the “normal” signal flow) which form cycles inthe network's topology. Computations derived from earlier input are fedback into the network, which gives an RNN a “short-term memory”.Feedback networks, such as RNNs, are dynamic; their ‘state’ is changingcontinuously until they reach an equilibrium point. For this reason,RNNs are particularly suited for detecting relationships across time ina given set of data. Recurrent networks take as their input not just thecurrent input example they see, but also what they have perceivedpreviously in time. The decision a recurrent net reached at time stept-1 may affect the decision it will reach one moment later at time stept. Thus, recurrent networks have two sources of input, the present andthe recent past, which combine to determine how they respond to newdata.

In an illustrative embodiment, the power delivery proposals 1212 may bepresented, by a presentation component 1208 of application 1204. Anadaptation component 1210 may be configured to receive input from a userto adapt the power delivery proposals 1212 if necessary. For example,changing a tolerated minimum charging time proposed by the powerdelivery module 1216 causes a recalculation of a power delivery proposal1212 that takes the new tolerated minimum charging time intoconsideration.

Feedback component 1218 optionally collects user or consumer feedbackrelative to the power delivery proposals 1212. In one embodiment,application 1204 may be configured not only to compute power deliveryproposals 1212 but also to provide a method for a user to inputfeedback, where the feedback is indicative of an accuracy of thecomputed power delivery proposals 1212. Feedback component 1218 appliesthe feedback in a machine learning technique such as to profiles or toM/L model 1206 to modify the M/L model 1206 for better proposals. In anillustrative embodiment, the application analyzes said feedback inputand the application reinforces the M/L model 1206 of the power deliverymodule 1216. If the feedback is satisfactory or unsatisfactory as to theaccuracy of the proposal, the application strengthens or weakensparameters of the M/L model 1206 respectively.

The input layer of the neural network model can be, for example, avector representative of a current, voltage or power, contextual weatheror calendar data provided by an NLP engine 1226, etc. In an example, aCNN (convolutional neural network) uses convolution to extract featuresfrom an input. In an embodiment, upon receiving a request to provide aproposal, the application creates an array of values that are input tothe input neurons of the M/L model 1206 to produce an array thatcontains the power delivery proposals 1212.

The neural network M/L model 1206 may be trained using various types oftraining data sets including stored profiles and a large number ofsample cell measurements. As shown in FIG. 13 , which depicts a blockdiagram of an example training architecture 1302 for machine-learningbased recommendation generation in accordance with an illustrativeembodiment, program code extracts various features 1306 from trainingdata 1304. The components of the training data 1304 have labels L. Thefeatures are utilized to develop a predictor function, H(x) or ahypothesis, which the program code utilizes as a M/L model 1308. Inidentifying various features in the training data 1304, the program codemay utilize various techniques including, but not limited to, mutualinformation, which is an example of a method that can be utilized toidentify features in an embodiment. Other embodiments may utilizevarying techniques to select features, including but not limited to,principal component analysis, diffusion mapping, a Random Forest, and/orrecursive feature elimination (a brute force approach to selectingfeatures), to select the features. “P” is the output that can beobtained, which when received, could further trigger the energymanagement system 124 to perform other steps such steps of a storedinstruction. The program code may utilize a machine learning m/lalgorithm 1312 to train M/L model 1308, including providing weights forthe outputs, so that the program code can prioritize various changesbased on the predictor functions that comprise the M/L model 1308. Theoutput can be evaluated by a quality metric 1310.

By selecting a diverse set of training data 1304, the program codetrains M/L model 1308 to identify and weight various features. Toutilize the M/L model 1308, the program code obtains (or derives) inputdata or features to generate an array of values to input into inputneurons of a neural network. Responsive to these inputs, the outputneurons of the neural network produce an array that includes the powerdelivery proposal to be presented or implemented contemporaneously.

Turning now to FIG. 14 , a M/L process 1400 is disclosed. The processmay begin at step 1402, wherein process 1400 may receive an energydemand state of one or more coupled electrical loads coupled to auniversal energy flow manager via a load application specific hardware(load ASH), the energy demand state being indicative of a desired amountof energy needed by the one or more coupled electrical loads in anenergy management environment. In step 1404, process 1400 may receive anavailable energy state of one or more coupled energy sources coupled tothe universal energy flow manager via a source application specifichardware (source ASH), the available energy state being indicative of anavailable amount of energy from the one or more coupled energy sourcesin the energy management environment. In step 1406, process 1400generates input data using at least the energy demand and availableenergy states. The available energy demand state and available energystates may include a voltage level, a current level, a power leveland/or a charging time. In step 1408, process 1400 may extract one ormore features from the input data, the one or more features beingrepresentative of a characteristic of a request for a power deliveryproposal operation. In step 1410, process 1400 may propose, using thepower delivery module, at least one power delivery proposal for the oneor more coupled electrical loads. In step 1412, process 1400 andperforms a power delivery operation based on the power deliveryproposal. The power delivery module may operate a machine learningengine. The power delivery operation may be performed through operatinga source ASH to obtain power for routing to a load ASH of the one ormore coupled electrical loads. The power delivery operation may also beperformed automatically and a plurality of loads, for example, aplurality of EVs may be powered by time division multiplexing ofload—source pairs.

Concerning step 1408, the one or more features may also representattributes obtained from an attribute prioritization 1502 step, as shownin FIG. 15 . In the attribute prioritization, one or more attributes1510 to consider for an output proposal operation may be obtained. Theone or more attributes may have different assigned priorities or weightsor may have the same or even unassigned priority or weight. By trainingthe M/L model 1308 with a large set of different datasets that considerthe attributes 1510, different scenarios can be handled by the powerdelivery module 1216. In an illustrative and non-limiting embodiment,the attributes 1510 include instructions to minimize charging time 1504,maximize available backup energy 1506 from energy sources, and minimizeutility cost 1508. Other attributes may include, for example, maximizingpay back or excess energy sales, maximize system safety, and maximizingbattery life.

Thus, in an illustrative embodiment, the power delivery module 1216operates based on a system of merits and demerits that functions tomaximize life, safety, utility costs, and other attributes while alsoconsidering other input parameters.

Charge Hailing Service

The illustrative embodiments recognize that as the use of electricvehicles rises, owners may be regularly presented with a task of findingavailable chargers for electric vehicles, especially when traveling awayfrom home. In some new environments, owners may not be readily able tofind chargers that meet their requirements. The illustrative embodimentsrecognize that even if public chargers may be available, they may belimited in number, capacity and may come with protracted waiting times,especially for in-demand chargers that may meet a fast chargingrequirement. Further, providing enough fast chargers to meet risingdemands may be an arduous task that may be insurmountable without anovel approach.

The illustrative embodiments may disclose a residential battery-basedcharging network for passenger and fleet services to ease chargingaccessibility and reduce downtime costs. The charging may comprise, DCfast charging and may expand a public DC Fast charging network toinclude privately owned and operated DCFC network which may in turnreduce downtime cost to delivery fleet and ride-hailing services, forexample. By employing, in a charge-hailing network, a plurality ofuniversal energy flow managers 126 and corresponding distributed sourcesof energy, a modular and scalable charging architecture may be providedto significantly increase availability of chargers and decrease waitingand charging times. Further remote locations may gain increasedavailability of chargers, grid outages may not significantly hinder EVcharging, less load may be exerted on utility systems and DC fastcharging may become more accessible. Even further, site or feederupgrades may not be necessary due to an ability of the universal energyflow manager to connect with independent energy sources without arequirement for matching specifications.

Turning to FIG. 16 , computing platform 1602 may be configured byapplication 1606 or a set of applications. Application 1606 may be anexample of a server application 116 of FIG. 1 or other application andmay include one or more instruction modules or communicate with one ormore remote, external or client instruction modules, of for example, theclient 110, dashboard 112, a personal mobile device, and the residentialcharger 1618. The instruction modules may include computer programmodules. The instruction modules may include one or more of a chargehailing module 1608, a charge reporting module 1610, a constraintsanalysis module 1612, a presentation module 1614, a charging module 1616and/or other instruction modules. In an aspect, an EVoperator/owner/customer/client may have a device that needs charging.Residential chargers 1618 comprising one or more universal energy flowmanagers 126 may periodically update a central application through thecharge reporting module 1610 with residential charger status parametersincluding, for example, remaining energy, availability, location, DCfast chargers, connected energy sources, state of charge, costs, stateof health or otherwise other residential charger status parameters. Theoperator may request a charging need, by the charge hailing module 1608,and a location and charge required (e.g., 10 minutes of charging, 20miles equivalent of charging, etc.) may be shared with the application1606 through the charge hailing module 1608.

Based on the updates from the residential chargers 1618 and the operatorrequest, the application 1606 may obtain, by the constraints analysismodule 1612, a set of available residential chargers 1618. Constraintsanalysis may comprise identifying the most important limiting factor(i.e., constraint) that may inhibit achieving a goal and thensystematically improving that constraint until it is no longer thelimiting factor, i.e., a constraint may be a restriction on the degreeof freedom in providing a solution. For example, an operator may requestDC fast charging which can be completed within the next two hours. Theconstraints analysis module 1612 may determine a distance and DC Fastcharging capability as constraints. The constraints analysis module 1612may determine a distance of the operator from one or more availableresidential chargers 1618 and a power level of an HVDC Bus of auniversal energy flow manager 126 of the one or more availableresidential chargers 1618. In another example, a residential chargerowner may have a preference that available back charge may not fallbelow a defined threshold level after completion of any charge hailingservice. Based on an amount of charge needed by a client/charge hailerand a number of energy sources connected to the residential charger1618, constraints analysis may determine that the threshold level may beexceeded upon completion of a future charge hailing operation and maythus remove said residential charger from consideration. In some otherexamples, the constraints analysis may weigh the importance ofidentified constraints and address them in order of importance. In somecases, the determined set of available residential chargers may bepresented along with an indication of how well each meets the identifiedconstraints. This and other examples may be performed through definedinstructions or by machine learning using example constraints as inputto a trained deep neural network. Of course, these are only examples asother constraints such as charging costs and other analysis techniquesmay be determined or requested.

A set of residential chargers 1618 that meet the constraints may bepresented, by the presentation module 1614, to the operator. Aresidential charger may be selected from the set and the operator maytransport the electric vehicle to the residential charger for charging,based on instructions from the charging module 1616. Alternatively, anowner of the residential charger may transport a standalone residentialcharger 1618, having at least an on-board energy storage device 306 tothe EV operator for charging. Other factors such as EV owner rating andresidential charger owner rating may be considered as discussed herein.

In some implementations, computing platform(s) 1602, remote platform(s)1620, residential chargers 1618 and/or external resources 1622 may beoperatively linked via one or more electronic communication links. Forexample, such electronic communication links may be established, atleast in part, via the network/communication infrastructure 102 of FIG.1 such as the Internet and/or other networks. It will be appreciatedthat this is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which computing platform 1602,remote platform 1620, and/or external resources 1622 may be operativelylinked via some other communication media. A residential charger 1618may comprise a universal energy flow manager having an ability toconnect to multiple energy sources through an application specifichardware as herein and may be portable. The residential charger may beused in any location that has a conventional grid supply to augment theavailable energy/power that may otherwise be limited when relying ongrid power.

A given remote platform 1620 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given remote platform 1620 to interface with system 1600 and/orexternal resources 1622, and/or provide other functionality attributedherein to remote platform 1620. By way of non-limiting example, a givenremote platform 1620 and/or a given computing platform 1602 may be aserver but may alternatively comprise one or more of a desktop computer,a laptop computer, a handheld computer, a tablet computing platform, aNetBook, a Smartphone, a gaming console, and/or other computingplatforms.

External resources 1622 may include sources of information outside ofsystem 1600, external entities participating with system 1600, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 1622 may beprovided by resources included in system 1600.

Computing platform 1602 may include electronic storage unit 108, one ormore processors 1604, and/or other components. Computing platform 1602may include communication lines, or ports to enable the exchange ofinformation with a network and/or other computing platforms.Illustration of computing platform 1602 in FIG. 16 is not intended to belimiting. Computing platform 1602 may include a plurality of hardware,software, and/or firmware components operating together to provide thefunctionality attributed herein to computing platform 1602. For example,computing platform 1602 may be implemented by a cloud of computingplatforms operating together as computing platform 1602.

Storage unit 108 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofstorage unit 108 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with computingplatform 1602 and/or removable storage that is removably connectable tocomputing platform 1602 via, for example, a port (e.g., a USB port, afirewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronicstorage unit 108 may include one or more of optically readable storagemedia (e.g., optical disks, etc.), magnetically readable storage media(e.g., magnetic tape, magnetic hard drive, floppy drive, etc.),electrical charge-based storage media (e.g., EEPROM, RAM, etc.),solid-state storage media (e.g., flash drive, etc.), and/or otherelectronically readable storage media. Electronic storage 130 mayinclude one or more virtual storage resources (e.g., cloud storage, avirtual private network, and/or other virtual storage resources).Storage unit 108 may store software algorithms, information determinedby processor information received from computing platform 1602,information received from remote platform 1620, and/or other informationthat enables computing platform 1602 to function as described herein.

Processor 1604 may be configured to provide information processingcapabilities in computing platform 1602. As such, processor 1604 mayinclude one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Although processor1604 is shown in FIG. 16 as a single entity, this is for illustrativepurposes only. In some implementations, processor 1604 may include aplurality of processing units. These processing units may be physicallylocated within the same device, or processor 1604 may representprocessing functionality of a plurality of devices operating incoordination. Processor 1604 may be configured to execute one or more ofcharge hailing module 1608, charge reporting module 1610, constraintsanalysis module 1612, presentation module 1614, charging module 1616.Processor 1604 may be configured to execute the modules by software;hardware; firmware; some combination of software, hardware, and/orfirmware; and/or other mechanisms for configuring processingcapabilities on processor 1604. As used herein, the term “module” mayrefer to any component or set of components that perform thefunctionality attributed to the module. This may include one or morephysical processors during execution of processor readable instructions,the processor readable instructions, circuitry, hardware, storage media,or any other components.

It should be appreciated that although modules 1608, 1610, 1612, 1614,and/or 1616 are illustrated in FIG. 16 as being implemented within asingle processing unit, in implementations in which processor 1604includes multiple processing units, one or more of modules 1608, 1610,1612, 1614, and/or 1616 may be implemented remotely from the othermodules. The description of the functionality provided by the differentmodules 1608, 1610, 1612, 1614, and/or 1616 described below is forillustrative purposes, and is not intended to be limiting, as any ofmodules 1608, 1610, 1612, 1614, and/or 1616 may provide more or lessfunctionality than is described. For example, one or more of modules1608, 1610, 1612, 1614, and/or 1616 may be eliminated, and some or allof its functionality may be provided by other ones of modules 1608,1610, 1612, 1614, and/or 1616. As another example, processor 1604 may beconfigured to execute one or more additional modules that may performsome or all of the functionality attributed below to one of modules1608, 1610, 1612, 1614, and/or 1616.

FIG. 17 illustrates a process for hailing a charge for an electricvehicle in accordance with one or more implementations. The operationsof process 1700 presented below are intended to be illustrative. In someimplementations, process 1700 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed

Process 1700 may begin at step 1702 by receiving, by a charge hailingservice of the charge hailing module 1608, a charge request from a firstuser device. In some implementations, the charge request may include atleast a measure of an amount of charge required for an electric vehicle132. The user device may be, for example, a personal mobile device or adashboard of an electric vehicle. In step 1704, process 1700 mayreceive, by a charge reporting service, a charge capacity update from aplurality of residential chargers comprising at least a firstresidential charger. In step 1706, process 1700 may compute, byconstraints analysis using a charge management service, a set ofresidential chargers for the first user device. In step 1708, process1700 may present, by a presentation service the set of residentialchargers to the first user device. In step 1710, process 1700 mayreceive, selection of the first residential charger from the presentedset of residential chargers. The selection may be made by the EVoperator or may be made automatically based on finding a charger thatbest fits the EV operator's constraints. A charging procedure may beperformed afterwards in step 1712.

In an aspect, process 1700 may comprise DC fast charging a firstelectric vehicle by the first residential charger. The process 1700 mayalso comprise DC fast charging a plurality of electric vehicles by thefirst residential charger based on allocating, by the charge managementservice, a plurality of first user devices to the same residentialcharger. In some implementations, the residential chargers 1618 mayprovide level 3 charging power of more than 15 kW, for example, morethan 20 kW or more than 50 kW or between 20 kW-200 kW or between 40 kWto 150 kW or a voltage of between 400V-900V with a charging speed of 3to 20 miles per minute.

In another aspect, the first user device may be operated by a user ofthe first electric vehicle and the first residential charger may beoperated by an owner of the first residential charger.

The process 1700 may perform the constraints analysis, for example, byperforming a computation of the set of residential chargers that meet aDC Fast charging requirement. Thus, real time charge parameterinformation about the residential chargers may be used for theconstraints analysis. Information about the charge parameter informationelectric vehicle and the electric vehicle owner may also be used for theconstraints analysis as needed. In an aspect, the constraints analysismay include performing a computation of a likelihood to a arrive at aresidential charger before a time limit or charge depletion threshold isreached.

The process 1700 may further comprise receiving, by the chargemanagement service of the charge management module 1624, and responsiveto receiving selection of the first residential charger, acceptance ofsaid selection from the first residential charger, and automaticallytransmitting, by the charge management service a location of the firstresidential charger to the first user device. The first electric vehicleof the user device may be transported to said location for charging. Inan aspect, the location may be obtained by triangulation.

In other implementations, the process may include receiving, by thecharge management service, and responsive to receiving selection of thefirst residential charger, acceptance of said selection from the firstresidential charger, and automatically transmitting, by the chargemanagement service a location of the first user device to the firstresidential charger. Herein the first residential charger may betransported to the electric vehicle for charging. This may be especiallyuseful in situations where the electric vehicle has depleted energy andis immobile. The charge management service may also receive the locationautomatically or by triangulation.

Further, by the use of a universal energy flow manager 126 able tocommunicate with a plurality of application specific hardware 114, anelectric vehicle load may be charged by another electric vehicle actingas a source. Even further, the low voltage DC Bus 514 may be utilized todirectly or indirectly provide low voltage DC fast charging toappliances as needed. Thus, any amount of power needed may be providedby the charge hailing services by configuring the residential chargerwith enough energy sources to meet the demand.

Turning now to FIG. 18 , a cloud charge hailing process 1800 is shown.The process may illustrate a charge hailing process managed from aserver according to an illustrative embodiment. Charge management module1624 may perform at least some of the server processes. The process maybegin at step 1802, wherein the process 1800 may wait for a request forcharge from a client. In step 1804, the process 1800 may determine a newrequest for charge and may perform, in step 1806, constraints analysisbased on input comprising client/customer/EV charging parameters andresidential charger status parameters.

Upon determining, in step 1808, that chargers that meet the request areavailable the process may send, in step 1812, the charger information tothe client. This information may be stripped of location data forprivacy. However, should there be no chargers available, the process1800 may send a no charger available feedback to the client in step 1810and wait for another request.

In step 1814, the process may determine that a client has selected acharger. The selection may be automatic or manual. In step 1816, theprocess may transmit the client information to the residential charger1618 for manual or automatic acceptance. The acceptance may be based ona reliability score, for example, a rating of the client. In step 1818,the process may determine that the charger has accepted the client for afuture charge procedure. In step 1820, process 1800 may transmit thecharger location to the client and may reserve charger for a definedtimeout period. In step 1822, the process 1800 may determine that theclient has arrived within the timeout period and may then validate instep 1824, based on predefined validation logic, an identification ofthe client.

Upon a validation pass in step 1826, process 1800 may instruct theresidential charger 1618 to perform a charging process in step 1828 tomeet the client request. When completed (step 1830), a post chargingoperation may be performed in step 1832. This may comprise, for example,paying a cost of the charging operation and/or rating the client orcharger. In step 1834, the charger may be marked as available, and theprocess may start over.

FIG. 19 illustrates an electric vehicle charge hailing process 1900which may be performed from a client device such as a personal mobilephone and/or an EV dashboard. Of course, the process is illustrative andis not meant to be limiting. Other similar processes may be possible inview of the descriptions herein. The electric vehicle charge hailingprocess 1900 may begin at step 1902, wherein the client may transmit acharging request. Upon determining that a defined timeout period hasbeen reached (step 1904), the client may be notified of the servicebeing unavailable (step 1908). The client may however be presented witha set of residential chargers 1618, in step 1906, that may meet thedemands of the charging request. The client may select a charger fromthe set of residential chargers in step 1910 and the selected chargermay accept the proposed charging service in step 1914 within a timeoutperiod (step 1912). Acceptance by the selected charger may be based on,for example, a reliability score of the client.

In step 1916, the EV may be transported to the selected charger for acharging operation. In some embodiments however, the charger may be aportable charger and may be transported to the client for a chargingoperation. In step 1918, the process 1900 performs a validationoperation in step 1920 to validate the client and or selected charger.This may be performed, for example, by an inspection of client and/orresidential charger ID (identification). Upon passing the validation,the charging operation may be performed in step 1922 until completion instep 1924 In step 1926, the process may perform a post chargingoperation and an optional feedback operation in step 1928.

FIG. 20 illustrates an example residential charge hailing process 2000which may be performed from a residential charger 1618. The process maybegin at step 2002 wherein the process 2000 may transmit at regular timeintervals (for example, every 1 second or every 30 seconds) an updateabout a residential charger parameter status, including for example, atotal number of connected energy sources and corresponding energyparameters and owner preferences. Upon receiving a request for charge instep 2004, the process may automatically or manually accept, in step2006, the request based on for example, a reliability score of theclient associated with the request. The process 2000 may wait in step2008 for defined timeout period (step 2010) until the client arrives instep 2012. Of source, in some embodiment, the charger may be transportedto the client.

In step 2014, the process 2000 may validate the charging process todetermine if charging is authorized and the charging operation may beperformed in step 2016 upon passing the validation. When the charge iscompleted in step 2018, statistics of the charge may be reported back tothe server, step 2020.

Thus, a computer-implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forintelligent power delivery proposals and other related features,functions, or operations. Where an embodiment of a portion thereof isdescribed with respect to a type of device, the computer-implementedmethod, system or apparatus, the computer program product, or a portionthereof, are adapted or configured for use with a suitable andcomparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail) or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructure,including the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present disclosure may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include the computer-readable storagemedium (or media) having the computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer-readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer-readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, including but not limited tocomputer-readable storage devices as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide, or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network, and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

The computer-readable program instructions for carrying out operationsof the present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object-oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer-readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer, and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer-readable program instructions by utilizing state information ofthe computer-readable program instructions to personalize the electroniccircuitry, to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein concerningflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that computer readable programinstructions can implement each block of the flowchart illustrationsand/or block diagrams and combinations of blocks in the flowchartillustrations and/or block diagrams.

These computer-readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other devicesto cause a series of operational steps to be performed on the computer,other programmable apparatus or other devices to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1-29. (canceled)
 30. A computer-implemented method comprising: receivingan energy demand state of one or more coupled electrical loads coupledto a universal energy flow manager via a load application specifichardware (load ASH), the energy demand state being indicative of adesired amount of energy needed by the one or more coupled electricalloads in an energy management environment; receiving an available energystate of one or more coupled energy sources coupled to the universalenergy flow manager via a source application specific hardware (sourceASH), the available energy state being indicative of an available amountof energy from the one or more coupled energy sources in the energymanagement environment; generating input data using at least the energydemand and available energy states; extracting one or more features fromthe input data, the one or more features representative of acharacteristic of a request for a power delivery proposal operation,proposing, using the power delivery module, at least one power deliveryproposal for the one or more coupled electrical loads; and performing apower delivery operation based on the power delivery proposal.
 31. Thecomputer-implemented method of claim 30, wherein the power deliverymodule is a machine learning engine.
 32. The computer-implemented methodof claim 30, further comprising: performing the power delivery operationthrough a load ASH of the one or more coupled electrical loads.
 33. Thecomputer-implemented method of claim 32, wherein the power deliveryoperation is performed automatically.
 34. The computer-implementedmethod of claim 30, further comprising: generating, by attributesprioritization, a set of attributes of the energy management environmentto enforce; and proposing the at least one power delivery proposal basedon one or more attributes of the set of attributes.
 35. Thecomputer-implemented method of claim 34, wherein the attributes includean attribute selected from the list consisting of minimizing utilitycost, minimizing charging time, maximizing availability of backupenergy, maximize excess energy sales, and maximizing battery life. 36.The computer-implemented method of claim 30, wherein the power deliveryproposal comprises instructions to perform a DC fast charge or one ormore electric vehicles.
 37. The computer-implemented method of claim 30,wherein the power delivery proposal comprises instructions to maximizeuse of renewable energy sources during a defined time interval.
 38. Thecomputer-implemented method of claim 30, wherein the power deliveryproposal is provided in real time.
 39. The computer-implemented methodof claim 30, wherein the power delivery proposal comprises load sheddinginstructions.
 40. The computer-implemented method of claim 30, whereinthe power delivery proposal comprises powering a plurality of loads bytime division multiplexing of load source pairs.
 41. Thecomputer-implemented method of claim 30, wherein the input data furthercomprise data selected from the list consisting of historical device usedata, a fast charging requirement, a weather forecast, a calendar data,a current electricity consumption demand, a vehicle energy demandprofile, a home energy demand profile, and a battery lifetime.
 42. Thecomputer-implemented method of claim 30, further comprising: providingfeedback for the power delivery module indicative of an accuracy ofproposals to reinforce the power delivery module.
 43. Thecomputer-implemented method of claim 30, further comprising: providingthe power delivery proposal in real time.
 44. A computer systemcomprising a processor configured to: receive an energy demand state ofone or more coupled electrical loads coupled to a universal energy flowmanager via a load application specific hardware (load ASH), the energydemand state being indicative of a desired amount of energy needed bythe one or more coupled electrical loads in an energy managementenvironment; receive an available energy state of one or more coupledenergy sources coupled to the universal energy flow manager via a sourceapplication specific hardware (source ASH), the available energy statebeing indicative of an available amount of energy from the one ore oneor more coupled energy sources in the energy management environment;generate input data using at least the energy demand and availableenergy states; extract one or more features from the input data, the oneor more features representative of a characteristic of a request for apower delivery proposal operation, propose, using the power deliverymodule, at least one power delivery proposal for the one or more coupledelectrical loads; and perform a power delivery operation based on thepower delivery proposal.
 45. A non-transitory computer-readable storagemedium storing a program which, when executed by a computer system,causes the computer system to: receive an energy demand state of one ormore coupled electrical loads coupled to a universal energy flow managervia a load application specific hardware (load ASH), the energy demandstate being indicative of a desired amount of energy needed by the oneor more coupled electrical loads in an energy management environment;receive an available energy state of one or more coupled energy sourcescoupled to the universal energy flow manager via a source applicationspecific hardware (source ASH), the available energy state beingindicative of an available amount of energy from the one ore one or morecoupled energy sources in the energy management environment; generateinput data using at least the energy demand and available energy states;extract one or more features from the input data, the one or morefeatures representative of a characteristic of a request for a powerdelivery proposal operation, propose, using the power delivery module,at least one power delivery proposal for the one or more coupledelectrical loads; and perform a power delivery operation based on thepower delivery proposal. 46-70. (canceled)